Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/52453
DC FieldValueLanguage
dc.contributor.authorRavelo-García, A. G.-
dc.contributor.authorSaavedra-Santana, P.-
dc.contributor.authorJuliá-Serdá, G.-
dc.contributor.authorNavarro-Mesa, J. L.-
dc.contributor.authorNavarro-Esteva, J.-
dc.contributor.authorÁlvarez-López, X.-
dc.contributor.authorGapelyuk, A.-
dc.contributor.authorPenzel, T.-
dc.contributor.authorWessel, N.-
dc.date.accessioned2018-11-25T20:28:22Z-
dc.date.available2018-11-25T20:28:22Z-
dc.date.issued2014-
dc.identifier.issn1054-1500-
dc.identifier.otherWoS-
dc.identifier.urihttp://hdl.handle.net/10553/52453-
dc.description.abstractMany sleep centres try to perform a reduced portable test in order to decrease the number of overnight polysomnographies that are expensive, time-consuming, and disturbing. With some limitations, heart rate variability (HRV) has been useful in this task. The aim of this investigation was to evaluate if inclusion of symbolic dynamics variables to a logistic regression model integrating clinical and physical variables, can improve the detection of subjects for further polysomnographies. To our knowledge, this is the first contribution that innovates in that strategy. A group of 133 patients has been referred to the sleep center for suspected sleep apnea. Clinical assessment of the patients consisted of a sleep related questionnaire and a physical examination. The clinical variables related to apnea and selected in the statistical model were age (p < 10(-3)), neck circumference (p < 10(-3)), score on a questionnaire scale intended to quantify daytime sleepiness (p < 10(-3)), and intensity of snoring (p < 10(-3)). The validation of this model demonstrated an increase in classification performance when a variable based on non-linear dynamics of HRV (p < 0.01) was used additionally to the other variables. For diagnostic rule based only on clinical and physical variables, the corresponding area under the receiver operating characteristic (ROC) curve was 0.907 (95% confidence interval (CI) = 0.848, 0.967), (sensitivity 87.10% and specificity 80%). For the model including the average of a symbolic dynamic variable, the area under the ROC curve was increased to 0.941 (95% = 0.897, 0.985), (sensitivity 88.71% and specificity 82.86%). In conclusion, symbolic dynamics, coupled with significant clinical and physical variables can help to prioritize polysomnographies in patients with a high probability of apnea. In addition, the processing of the HRV is a well established low cost and robust technique.-
dc.languageeng-
dc.relation.ispartofChaos-
dc.sourceChaos[ISSN 1054-1500],v. 24 (2), (Junio 2014)-
dc.subject.otherElectroencephalography-
dc.subject.otherMultivariate analysis-
dc.subject.otherNonlinear dynamics-
dc.titleSymbolic dynamics marker of heart rate variability combined with clinical variables enhance obstructive sleep apnea screening-
dc.typeinfo:eu-repo/semantics/Article-
dc.typeArticle-
dc.identifier.doi10.1063/1.4869825-
dc.identifier.scopus84920545073-
dc.identifier.isi000338668900046-
dc.contributor.authorscopusid9634135600-
dc.contributor.authorscopusid56677724200-
dc.contributor.authorscopusid56756025600-
dc.contributor.authorscopusid6603171553-
dc.contributor.authorscopusid9634488300-
dc.contributor.authorscopusid56006484500-
dc.contributor.authorscopusid56590578700-
dc.contributor.authorscopusid16177319600-
dc.contributor.authorscopusid7005360676-
dc.contributor.authorscopusid7005373972-
dc.identifier.eissn1089-7682-
dc.description.lastpage024404-
dc.identifier.issue2-
dc.description.firstpage024404-
dc.relation.volume24-
dc.investigacionIngeniería y Arquitectura-
dc.type2Artículo-
dc.contributor.daisngid1986395-
dc.contributor.daisngid3094556-
dc.contributor.daisngid2942583-
dc.contributor.daisngid2630721-
dc.contributor.daisngid4253704-
dc.contributor.daisngid23492911-
dc.contributor.daisngid13174950-
dc.contributor.daisngid35791-
dc.contributor.daisngid188715-
dc.description.numberofpages8-
dc.utils.revision-
dc.contributor.wosstandardWOS:Ravelo-Garcia, AG-
dc.contributor.wosstandardWOS:Saavedra-Santana, P-
dc.contributor.wosstandardWOS:Julia-Serda, G-
dc.contributor.wosstandardWOS:Navarro-Mesa, JL-
dc.contributor.wosstandardWOS:Navarro-Esteva, J-
dc.contributor.wosstandardWOS:Alvarez-Lopez, X-
dc.contributor.wosstandardWOS:Gapelyuk, A-
dc.contributor.wosstandardWOS:Penzel, T-
dc.contributor.wosstandardWOS:Wessel, N-
dc.date.coverdateJunio 2014-
dc.identifier.ulpgc-
dc.contributor.buulpgcBU-TEL-
dc.description.sjr0,829
dc.description.jcr1,954
dc.description.sjrqQ1
dc.description.jcrqQ1
dc.description.scieSCIE
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.deptGIR Estadística-
crisitem.author.deptDepartamento de Matemáticas-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.orcid0000-0002-8512-965X-
crisitem.author.orcid0000-0003-1681-7165-
crisitem.author.orcid0000-0003-3860-3424-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.parentorgDepartamento de Matemáticas-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameRavelo García, Antonio Gabriel-
crisitem.author.fullNameSaavedra Santana, Pedro-
crisitem.author.fullNameNavarro Mesa,Juan Luis-
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